1 Final Review Econ 240A

2 Outline The Big Picture Processes to remember ( and habits to form) for your quantitative career (FYQC) Concepts to remember FYQC Discrete Distributions Continuous distributions Central Limit Theorem Regression

The Classical Statistical Trail Descriptive Statistics Inferential Statistics Probability Discrete Random Variables Discrete Probability Distributions; Moments Binomial Application Rates & Proportions

4 Where Do We Go From Here? Regression Properties Assumptions Violations Diagnostics Modeling Probability Count ANOVA Contingency Tables

5 Processes to Remember Exploratory Data Analysis Distribution of the random variable Histogram Lab 1 Stem and leaf diagram Lab 1 Box plot Lab 1 Time Series plot: plot of random variable y(t) Vs. time index t X-y plots: Y Vs. x 1, y Vs. x 2 etc. Diagnostic Plots Actual, fitted and residual

6 Concepts to Remember Random Variable: takes on values with some probability Flipping a coin Repeated Independent Bernoulli Trials Flipping a coin twice Random Sample Likelihood of a random sample Prob(e 1 ^e 2 …^e n ) = Prob(e 1 )*Prob(e 2 )…*Prob(e n )

7 Discrete Distributions Discrete Random Variables Probability density function: Prob(x=x*) Cumulative distribution function, CDF Equi-Probable or Uniform E.g x = 1, 2, 3 Prob(x=1) =1/3 = Prob(x=2) =Prob(x=3)

8 Discrete Distributions Binomial: Prob(k) = [n!/k!*(n-k)!]* p k (1-p) n-k E(k) = n*p, Var(k) = n*p*(1-p) Simulated sample binomial random variable Lab 2 Rates and proportions Poisson

9 Continuous Distributions Continuous random variables Density function, f(x) Cumulative distribution function Survivor function S(x*) = 1 – F(x*) Hazard function h(t) =f(t)/S(t) Cumulative hazard functin, H(t)

10 Continuous Distributions Simple moments E(x) = mean = expected value E(x 2 ) Central Moments E[x - E(x)] = 0 E[x – E(x)] 2 =Var x E[x – E(x)] 3, a measure of skewness E[x – E(x)] 4, a measure of kurtosis

11 Continuous Distributions Normal Distribution Simulated sample random normal variable Lab 3 Approximation to the binomial, n*p>=5, n*(1-p)>=5 Standardized normal variate: z = (x-  )/  Exponential Distribution Weibull Distribution Cumulative hazard function: H(t) = (1/  )  t  Logarithmic transform ln H(t) = ln (1/  )  +  lnt

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14 Central Limit Theorem Sample mean,

15 Population Random variable x Distribution f(    f ? Sample Sample Statistic: Sample Statistic Pop.

16 The Sample Variance, s 2 Is distributed chi square with n-1 degrees of freedom (text, 12.2 “inference about a population variance) (text, pp , Chi-Squared distribution)

17 Regression Models Statistical distributions and tests Student’s t F Chi Square Assumptions Pathologies

18 Regression Models Time Series Linear trend model: y(t) =a + b*t +e(t) Lab 4 Exponential trend model: y(t) =exp[a+b*t+e(t)] Natural logarithmic transformation ln Ln y(t) = a + b*t + e(t) Lab 4 Linear rates of change: y i = a + b*x i + e i dy/dx = b Returns generating process: [r i (t) – r f 0 ] =  +  *[r M (t) – r f 0 ] + e i (t) Lab 6

19 Regression Models Percentage rates of change, elasticities Cross-section Ln assets i =a + b*ln revenue i + e i Lab 5  dln assets/dlnrevenue = b = [dassets/drevenue]/[assets/revenue] = marginal/average

20 Linear Trend Model Linear trend model: y(t) =a + b*t +e(t) Lab 4

21 Lab 4

22 Lab Four t-test: H 0 : b=0 H A : b≠0 t =[ – 0]/ = -14 F-test: F 1,36 = [R 2 /1]/{[1-R 2 ]/36} = 196 = Explained Mean Square/Unexplained mean square

23 Lab 4

24 Lab 4

25 Lab 4 2.5%

26 Lab Four % 196

27 Exponential Trend Model Exponential trend model: y(t) =exp[a+b*t+e(t)] Natural logarithmic transformation ln Ln y(t) = a + b*t + e(t) Lab 4

28 Lab Four

29 Lab Four

30 Percentage Rates of Change, Elasticities Percentage rates of change, elasticities Cross-section Ln assets i =a + b*ln revenue i + e i Lab 5  dln assets/dlnrevenue = b = [dassets/drevenue]/[assets/revenue] = marginal/average

31 Lab Five Elasticity b = H 0 : b=1 H A : b<1 t 25 = [0.778 – 1]/0.148 = t-crit(5%) = -1.71

32 Linear Rates of Change Linear rates of change: y i = a + b*x i + e i dy/dx = b Returns generating process: [r i (t) – r f 0 ] =  +  *[r M (t) – r f 0 ] + e i (t) Lab 6

33 Watch Excel on xy plots! True x axis: UC Net

34 Lab Six r GE = a + b*r SP500 + e

35 Lab Six

36 Lab Six

37 View/Residual tests/Histogram-Normality Test

38 Linear Multivariate Regression House Price, # of bedrooms, house size, lot size P i = a + b*bedrooms i + c*house_size i + d*lot_size i + e i

39 Lab Six price bedrooms House_size Lot_size

40 Price = a*dummy2 +b*dummy34 +c*dummy5 +d*house_size01 +e

41 Lab Six C captures three and four bedroom houses

42 Regression Models How to handle zeros? Labs Six and Seven: Lottery data-file Linear probability model: dependent variable: zero-one Logit: dependent variable: zero-one Probit: dependent variable: zero-one Tobit: dependent variable: lottery See Project I PowerPoint application to vehicle type data

43 Regression Models Failure time models Exponential Survivor: S(t) = exp[- *t], ln S(t) = - *t Hazard rate, h(t) = Cumulative hazard function, H(t) = *t Weibull Hazard rate, h(t) = f(t)/S(t) = (  /  )(t/  )  -1 Cumulative hazard function: H(t) = (1/  )  t  Logarithmic transform ln H(t) = ln (1/  )  +  lnt